جستجوی استاد راهنمای دانشگاهی

Didier Trono is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Virology and Genetics (LVG) within the School of Life Sciences. Formerly, from 2004 to 2012, he served as the founding dean of EPFL's Faculty of Life Sciences, orchestrating its development and growth during this formative period. Trono received his medical education at the University of Geneva, followed by clinical training in pathology, internal medicine, and infectious diseases in Geneva and at Massachusetts General Hospital in Boston. His scientific career began at the Whitehead Institute of MIT, and in 1990 he was recruited by the Salk Institute of San Diego to launch an AIDS research center. After seven years in the United States, he returned to Europe and eventually joined EPFL. Dr. Trono's research has evolved significantly over his career. Initially focusing on virus-host interactions, he studied pathogens like HIV and Hepatitis B virus, creating HIV-derived genetic transfer tools that are now successfully used in gene therapy. For approximately the last fifteen years, his research has centered on epigenetics, particularly exploring the impact of retroelements and their control mechanisms on development and physiology of higher organisms, including humans. His laboratory investigates how transposable elements and KRAB zinc finger proteins regulate gene expression, with important implications for understanding cancer biology and developing new diagnostic and therapeutic approaches. Analysis of his recent publications (2022-2024) reveals a strong focus on transposable elements, KRAB zinc finger proteins, and their roles in gene regulation and cancer. His work combines molecular biology, genomics, and bioinformatics approaches to understand how these ancient viral remnants have been co-opted by the host genome to regulate development and cellular functions. The research spans basic molecular mechanisms to potential clinical applications in cancer diagnosis and therapy, with some recent work also addressing SARS-CoV-2 and immune responses. Throughout his career, Professor Trono has mentored numerous PhD students, including Bojkowska Karolina, Brandão Sanches Vong Martins Filipe Amândio, Bulliard Yannick, Coluccio Andrea, Corsinotti Andrea, Coudray Alexandre, De Tribolet-Hardy Jonas Caspar, and Dorschel Iris Arianna. His laboratory has received significant funding to support research at the intersection of virology, genetics, and epigenetics, contributing to EPFL's reputation as a leading institution in life sciences research. The Trono Laboratory continues to be at the forefront of research on retroelements and their regulatory mechanisms, maintaining a vibrant research environment that bridges fundamental biological questions with potential medical applications, particularly in cancer research and precision medicine.

• Epigenetics
• Retroelements
• Transposable elements
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Prof. Knut Drescher is an Associate Professor at the Biozentrum, University of Basel , leading a research group focused on bacterial biofilms , swarming , and microbial multicellularity . Previously, he served as a Professor of Biophysics and Max Planck Research Group Leader at Philipps-Universität Marburg (2015-2021) and conducted postdoctoral research at Princeton University. Research Interests: Physical and biological mechanisms of biofilm formation Cell-cell interactions in microbial communities Antibiotic resistance in biofilms Hydrodynamics of bacterial swarms Evolution of cooperation in multispecies biofilms Development of bioimaging software (BiofilmQ, BacStalk) Scientific Awards: 2023: SNSF Consolidator Grant 2019: Heinz Maier-Leibnitz Prize (DFG), VAAM Research Prize, IUPAP Young Scientist Prize 2016: ERC Starting Grant Advising & Grants: Advises PhD and Master's students in microbiology, biophysics, and bioinformatics Secured major grants from ERC , HFSP , and DFG

• Bacterial Biofilms
• Microbial Multicellularity
• Biophysics
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Christof Bigler is an Adjunct Professor at ETH Zurich's Department of Environmental Systems Science, where he also serves as Deputy Group Head of the Professorship of Forest Ecology and Senior Scientist. He leads the ETH Tree-Ring Laboratory and teaches courses in dendroecology, applied statistics, and forest ecology. His research focuses on tree mortality, climate impacts on forests, disturbance ecology, and phenology, with a strong emphasis on quantitative methods and long-term data analysis. Bigler holds a PhD in Forest Ecology from ETH Zurich (2003), awarded the ETH Medal for his thesis on tree mortality modeling. He has conducted postdoctoral research in Switzerland and the University of Colorado Boulder, exploring disturbance interactions and dendroecology. He has supervised numerous PhD and master's students, contributing to over 150 publications in top journals like Nature Climate Change and Ecology . His work integrates dendrochronological techniques with statistical modeling to understand forest responses to climate change, including drought effects, tree competition, and mortality patterns. He serves on editorial boards for journals like Agricultural and Forest Meteorology and reviews proposals for national and international science foundations. His research also includes managing forest reserves and studying natural forest dynamics in the Alps and beyond. Education: PhD in Forest Ecology, ETH Zurich (2003) Postdoctoral Research: University of Colorado Boulder (2004–2005) Diploma in Biology, ETH Zurich (1998) Key Awards: ETH Medal for PhD thesis (2003) Professional Roles: Member of the Tree-Ring Society, Editor for Frontiers in Forests , and Commission for Phenology and Seasonality (KPS). Bigler's lab and fieldwork emphasize long-term datasets, including analyses of tree-ring records from Swiss National Park and other reserves. His research highlights the critical role of historical climate data in predicting future forest resilience and adaptation strategies.

• Dendroecology
• Statistical Modeling
• Quantitative Ecology
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Simone Fior is a Lecturer at the Department of Environmental Systems Science , ETH Zürich , focusing on ecological genetics and plant adaptation. Their research integrates genomic, quantitative genetics, and ecological field experiments, particularly on Dianthus (Caryophyllaceae) along altitudinal and climatic gradients. Recent work explores climate-induced range shifts, local adaptation, and genomic responses to environmental changes. Professional experience includes roles at ETH Zürich since 2013 (Senior Assistant, Postdoc) and prior positions at the Edmund Mach Foundation (2009-2012) and University of Insubria (2007-2008). Education spans a PhD in Plant Biology (University of Milan, 2007) and an MSc in Natural Sciences (University of Milan, 2003). Simone co-organizes the Bioinformatics for Adaptation Genomics Winter School . Key research areas include adaptive divergence , polygenic adaptation , climate change biology , and phylogenomics . Articles emphasize genomic selection signatures, functional-structural modeling, and ecological-genetic interactions. Notable collaborations involve Jake Alexander, Alex Widmer, and interdisciplinary teams at ETH Zurich.

• Ecological Genetics
• Plant Adaptation
• Population Genomics
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Suliana Manley is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and the Laboratory of Experimental Biophysics . She also holds teaching and research roles in EPFL's School of Life Sciences and Swiss Plasma Center , focusing on interdisciplinary biophysical studies. Education : PhD in Physics (2004), Harvard University Bachelor's in Physics & Mathematics (1997), Rice University Manley's research centers on super-resolution fluorescence imaging , single-molecule tracking , and quantitative biophysics . Key themes include: Understanding protein assembly dynamics at cellular membranes Elucidating viral assembly mechanisms (e.g., HIV-Gag) Developing 3D imaging algorithms and high-density data reconstruction tools like PALMsiever and FALCON Quantifying nanoscale organization in systems like telomeres and centrioles Her work bridges optical physics , computational image analysis , and cellular biology , with notable Nature and PNAS publications. Collaborations span bioengineering , genetics , and medical research . Scientific Awards : Featured in Nature Methods Research Highlights (3x) Very Important Paper and Cover Article (ChemBioChem, 2012) Postdoctoral Fellow, NIH and MIT Advising & Collaborations : Current PhD students in biophysics, cellular biology, and bioengineering Former students: Anna Archetti, Aleksandr Benke, Andrea Callegari, and others Co-founder of tools for high-density super-resolution microscopy and live-cell imaging Labs & Teams : Leads the Laboratory of Experimental Biophysics at EPFL, integrating physics-based methods into biological questions. The lab focuses on quantitative imaging , computational modeling , and software development for nanoscale analysis.

• Super-resolution Microscopy
• Single-molecule Biophysics
• Protein Assembly
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Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

• Machine Learning
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Louis Du Plessis is a Lecturer at ETH Zürich's Department of Biosystems Science and Engineering in Basel, Switzerland. His research focuses on computational evolution with particular emphasis on infectious disease dynamics and genomic analysis. He maintains an active research profile with numerous high-impact publications in top-tier journals. Dr. Du Plessis completed his doctoral studies at ETH Zürich in 2016 with a thesis titled 'Understanding the spread and adaptation of infectious diseases using genomic sequencing data,' building upon his 2011 Master's work on evolutionary rate variation. His current research sits at the intersection of computational biology, epidemiology, and evolutionary genetics. His research interests span computational epidemiology, phylodynamics, viral evolution, and infectious disease modeling. He has made significant contributions to understanding pandemic dynamics, particularly regarding influenza and SARS-CoV-2, using genomic and epidemiological data integration. His methodological work includes developing computational approaches for estimating epidemic dynamics and viral transmission patterns. Analysis of his recent publications reveals a strong focus on how pandemics disrupt normal viral circulation patterns, with particular attention to influenza evolution during the 2009 H1N1 and COVID-19 pandemics. His work often combines phylogenetic analysis with epidemiological modeling to extract maximum information from genomic and case count data. Dr. Du Plessis has received research funding from European Commission projects including 'From Foundations of Phylodynamics to new applications in Cell Biology' (grant 101001077) and 'MOnitoring Outbreak events for Disease surveillance in a data science context' (grant 874850). He is actively involved in developing computational tools for analyzing pathogen genomic data and has contributed to several software packages used in the field. His work has significant implications for public health surveillance and pandemic preparedness.

• Computational Epidemiology
• Phylodynamics
• Viral Evolution
• +۳ مورد دیگر

Kjell Jorner is an Assistant Professor of Digital Chemistry in the Institute for Chemical and Bioengineering at ETH Zurich's Department of Chemistry and Applied Biosciences. His research group focuses on integrating computational methods and machine learning to address challenges in chemical synthesis, materials design, and reaction prediction. Education: PhD from Uppsala University (Photochemistry of aromatic compounds) Postdoctoral studies at AstraZeneca UK (Reaction prediction using computational chemistry and ML) Postdoctoral studies at University of Toronto (Molecular design of catalysts and organic electronic materials) Research Interests: Professor Jorner's work bridges computational chemistry, machine learning, and experimental design. Key areas include: Development of quantum mechanics-machine learning hybrid approaches for reaction feasibility prediction Inverse molecular design of functional materials (e.g., singlet-fission systems) Computational catalyst optimization and high-throughput screening methods Digital tools for chemical education and cheminformatics Publication Trends (2023-2025): Recent articles demonstrate a strong focus on machine learning applications in chemistry, including reaction prediction algorithms, catalyst design frameworks, and automated molecular generation. A recurring theme is the development of computational tools to accelerate materials discovery and optimize chemical processes. Laboratory & Team: Leads the Digital Chemistry research group at ETH Zurich (HCI E 137) exploring computational approaches to chemical challenges.

• Digital Chemistry
• Computational Chemistry
• Machine Learning
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Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement

• Plant Developmental Biology
• Epigenetics
• Plant Genetics
• +۵ مورد دیگر

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

• Machine Learning
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Anita Rauch is a Full Professor of Medical Genetics and Director of the Institute of Medical Genetics at the University of Zurich. Her research focuses on the genetic and molecular mechanisms underlying severe neurodevelopmental disorders and hereditary breast cancer, utilizing genome sequencing and human induced pluripotent stem cell (iPSC) models. Current projects include: Genetic causes and molecular mechanisms in severe intellectual disability Praeclare (genetic variant clarification for prenatal medicine) SwissGenVar (clinical genetic variant interpretation platform) Hereditary breast cancer in Switzerland Her work is funded by the Swiss National Science Foundation (SNSF). She leads a multidisciplinary team conducting functional studies on novel candidate genes (e.g., CYFIP2, TMEM94) and exploring genotype-phenotype correlations through iPSC-derived neuronal models. Key research trends in her publications include: Clinical trials for cancer therapies (colon, esophageal, pancreatic, breast) Pharmacogenetic determinants of chemotherapy toxicity Non-inferiority trial design methodology Genetic variant interpretation in personalized medicine She mentors PhD and Master’s students in medical genetics and contributes to the advancement of precision oncology and neurogenetics through translational research.

• Medical Genetics
• Neurodevelopmental Disorders
• Genome Sequencing
• +۳ مورد دیگر

Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel's Faculty of Business and Economics (WWZ), where he leads research at the intersection of finance, computational methods, and artificial intelligence. His work focuses on risk management, portfolio optimization, algorithmic trading, and financial simulations. His research interests span computational finance, artificial intelligence in finance, data analysis, risk management, portfolio optimization, algorithmic and high-frequency trading, financial networks, complex adaptive systems, and market simulations. He applies advanced computational and heuristic optimization techniques to solve real-world financial problems, contributing significantly to quantitative finance and financial engineering. His recent publications demonstrate a consistent focus on applying evolutionary algorithms, reinforcement learning, and numerical optimization to portfolio management, market impact modeling, and financial forecasting. The research integrates econometrics, machine learning, and financial theory, emphasizing practical implementation and robust risk-aware decision-making. Several best-paper awards Maringer has served as Chair of the Portfolio Optimization Section of the IEEE Computational Economics and Finance Technical Committee from 2008 to 2018 and is frequently involved in organizing and program committees of international conferences. He has advised or collaborated with numerous researchers, though specific student names are not listed. His research has been supported through academic affiliations and likely institutional or conference-based grants, though explicit funding sources are not detailed. He is affiliated with several research groups, including IEEE Computational Economics and Finance TC, COMISEF, ERCIM, Centre for Innovative Finance, and the European Financial Management Association, reflecting a broad collaborative network in computational finance and economics.

• Computational Economics
• Computational Finance
• Artificial Intelligence in Finance
• +۸ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Prof. Zerihun Tadele is a Professor and Group Leader in the Department of Crop Breeding and Genomics at the University of Bern's Institute of Plant Sciences. His research focuses on improving the orphan African cereal Eragrostis tef (tef) through genomic, agronomic, and biotechnological approaches. Key areas include drought tolerance, genetic diversity, and sustainable agricultural practices. He leads projects on tef variety development, mutant breeding, and mycorrhizal symbiosis effects. Research emphasizes Genetic improvement of tef and other underutilized crops Omics technologies for crop resilience Climate-smart agricultural techniques Seed systems and policy for orphan crops Recent work highlights innovations like somatic embryogenesis for Ethiopian potato and nitrogen-use efficiency studies. His team collaborates on global initiatives to enhance food security through underutilized crops. Publications (2022–2025) span over 50 peer-reviewed articles, focusing on tef genetics, breeding strategies, and environmental adaptation. He actively promotes tef as a strategic crop for Africa's food systems.

• Plant Breeding
• Genomics
• Agricultural Science
• +۴ مورد دیگر